The subject matter discussed in the background section should not be assumed to be prior art merely as a result of its mention in the background section. Similarly, a problem mentioned in the background section or associated with the subject matter of the background section should not be assumed to have been previously recognized in the prior art. The subject matter in the background section merely represents different approaches, which in and of themselves may also correspond to implementations of the claimed technology.
Users may interact with electronic devices, such as a computer or a television, or computing applications, such as computer games, multimedia applications, or office applications, via their gestures. Typically, the user's gestures may be detected using an optical imaging system, and characterized and interpreted by suitable computational resources. For example, a user near a TV may perform a sliding hand gesture, which is detected by the motion-capture system; in response to the detected gesture, the TV may activate and display a control panel on the screen, allowing the user to make selections thereon using subsequent gestures; for example, the user may move her hand in an “up” or “down” direction, which, again, is detected and interpreted to facilitate channel selection.
While utilization of user gestures to interact with electronic devices and/or computing applications has generated substantial consumer excitement and may ultimately supplant conventional control modalities that require physical contact between the user and a control element, many current motion-capture systems suffer from excessive processing time and/or low detection sensitivity. For example, a motion-capture system may detect a user's gestures regardless of the actual distance traversed by the user's movement. If the user's gestures in fact occupy a small fraction of the working volume in which a system can detect gestures, analyzing the entire working volume over a sequence of processed images wastes computational resources. In addition, because the spatial region within which a user's gestures take place can vary; some users may perform a gesture on a small scale, while other users may traverse a much larger spatial region in performing the same gesture. Accordingly, the user's gestural intent typically cannot be inferred merely from the detected distance traversed by, for example, the user's finger. Interpreting gestural intent without either wasting computational resources or ignoring relevant portions of a user's movement represents a substantial challenge.
Consequently, there is a need for a motion-capture system that detects gestures in a determined subset of the working volume whose size and location corresponds to the region where particular users perform gesture-related movements.